Most real-world networks are incompletely observed. Algorithms that can accurately predict
which links are missing can dramatically speed up network data collection and improve …

As scientific publication rates increase, knowledge acquisition and the research
development process have become more complex and time-consuming. Literature-Based …

Link prediction is an important learning task for graph-structured data. In this paper, we
propose a novel topological approach to characterize interactions between two nodes. Our …

In this review, we discuss the opportunity for repurposing drugs for use in l-DOPA-induced
dyskinesia (LID) in Parkinson's disease. LID is a particularly suitable indication for drug …

Link prediction is a paradigmatic and challenging problem in network science, which aims to
predict missing links, future links and temporal links based on known topology. Along with …

Repurposing regulatory agency-approved molecules, with proven safety in humans, is an
attractive option for develo** new treatments for disease. We identified and assessed the …

Scientific progress depends on formulating testable hypotheses informed by the literature. In
many domains, however, this model is strained because the number of research papers …

Data science, a new discovery paradigm, is potentially one of the most significant advances
of the early twenty-first century. Originating in scientific discovery, it is being applied to every …

Link mining is an important task in the field of data mining and has numerous applications in
informal community. Suppose a real-world complex network, the responsibility of this …

Purpose Drug repurposing is an effective means of increasing treatment options for
diseases, however identifying candidate molecules for the indication of interest from the …